RNN做cifar-10图像分类任务pytorch版本的代码

以下是使用PyTorch实现的RNN模型进行CIFAR-10图像分类的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义超参数
input_size = 32
hidden_size = 100
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 5
learning_rate = 0.001

# 加载数据集并进行预处理
transform = transforms.Compose(
    [transforms.Resize((input_size, input_size)),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
                                           shuffle=True)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                            download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
                                          shuffle=False)

# 定义RNN模型
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        # 初始化隐状态
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

        # 前向传播RNN模型
        out, _ = self.rnn(x, h0)

        # 解码最后一个状态
        out = self.fc(out[:, -1, :])

        return out

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.reshape(-1, input_size, input_size).to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.reshape(-1, input_size, input_size).to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))

此代码使用了PyTorch内置的RNN模块，并将其应用于CIFAR-10图像分类任务。在训练过程中，我们使用Adam优化器和交叉熵损失函数。在测试过程中，我们计算测试集上的准确率。